What Data for This Category Looks Like
Data for this category originates primarily from object storage, data warehouses, annotation platforms, and customer acceptance feedback. Data updates occur once per batch delivery or per version iteration, on a regular schedule. Supporting daily documentation includes dataset descriptions, version records, delivery checklists, and acceptance reports. Common data fields include version number, total sample count, single-sample size, data source identifier, delivery time, and acceptance status. Units include items, MB, days, and others. Fields across all batches must remain consistent to ensure traceability.
Constraints Imposed by These Characteristics on Tool Calling and Plugins Workflows
Because datasets must be traced by version and aligned with customer acceptance requirements, tool calls must be fixedly linked to the dataset path of the corresponding version. This prevents data mixing caused by cross-version calls. Field differences across multiple batches of data must be validated in advance within plugin configurations, to stop delivery exceptions from missing fields or format errors. Version records must be updated synchronously with tool calls, to ensure one-to-one correspondence between call logs and version records. This supports subsequent defect tracing. Additionally, delivery checklists must be automatically pulled and verified via plugins, to reduce manual verification time and errors, and improve delivery accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Plugin Trigger Timing | Automatically trigger after dataset version update | Ensure each version iteration synchronizes tool calls, avoiding mismatches between versions and call results |
| Field Validation Rules | Validate required fields: version number, sample count, acceptance status | Preemptively block delivery exceptions caused by missing fields |
toolChoice | Fixed as auto | Allow the model to autonomously select appropriate tools to complete dataset delivery verification |
functionCall | Enable mandatory verification mode | Ensure tool calls strictly follow the preset dataset delivery workflow |
| Failure Retry Count | 3 times, 60-second interval | Address temporary call failures caused by network fluctuations |
| Delivery Checklist Generation | Automatically pull dataset metadata via tool calls to generate | Replace manual verification, improve efficiency of delivery completeness checks |
Three Common Misconfigurations
- The plugin edit page always shows an "unsaved" status, even after clicking the save button. This occurs because the synchronization logic that binds plugin configuration to dataset versions fails to load correctly, leading to a mismatch between the page status and background configuration.
- A connection refused error is returned when calling the dataset delivery tool, with the log displaying
connect ECONNREFUSED 172.23.0.2:3001. This happens because the target plugin service is not running normally, or internal port access is restricted by network policies. - The delivery checklist generated by tool calls omits the sample difference description field. This occurs because the field validation rule was not enabled in the configuration, and the required status of this field was not verified in advance, resulting in missing key delivery information.
How to Verify Proper Configuration
- Manually trigger a plugin call, and confirm the generated delivery checklist includes all preset required fields.
- In FastGPT v4.17.0, review plugin call logs to confirm each call synchronously updates the version record for the corresponding version.
- Simulate a plugin call failure scenario, and confirm the system executes retry operations according to the configured retry count and interval.
- Check the status indicator on the plugin configuration page, and confirm the status changes to saved after saving.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.